Real-time people counting using blob descriptor

Abstract We propose a system for counting the number of pedestrians in real-time. This system estimates “how many pedestrians are and where they are in video sequences” by the following procedures. First, candidate regions are segmented into blobs according to background subtraction. Second, a set of features are extracted from each blob and a neural network estimates the number of pedestrians corresponding to each set of features. To realize real-time processing, we used only simple and valid features, and the adaptive background modeling using Parzen density estimation, which realizes fast and accurate object detection in input images. We also validate the effectiveness of the proposed system by several experiments.

[1]  Atsushi Shimada,et al.  A fast algorithm for adaptive background model construction using parzen density estimation , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[2]  Ramakant Nevatia,et al.  Bayesian human segmentation in crowded situations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Jean-Philippe Thiran,et al.  Counting Pedestrians in Video Sequences Using Trajectory Clustering , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[5]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Nuno Vasconcelos,et al.  Mixtures of dynamic textures , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[7]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[8]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[9]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[10]  Oliver Schreer,et al.  Fast and robust shadow detection in videoconference applications , 2002, International Symposium on VIPromCom Video/Image Processing and Multimedia Communications.